Extended Abstract Background: Due to the increasing demand for animal products, reproductive efficiency considered as one of the main objectives of sheep breeding, even in cases where the main emphasis is on milk or wool production. In cases the meat production is the main breeding objective, increasing reproductive efficiency is the most important goal of breeding programs. Although increasing fertility in ewe is possible in different pathways, ovulation rate and liter size are the main goals in breeding programs to achieve high fertility. Then, this study was performed to detection selection signature between Iranian native (as a genetic group with low reproductive performance) and Icelandic (as a genetic group with high reproductive performance) sheep breeds, and identify genes related to fecundity and litter size in sheep. Methods: In this regard, the genomic information of 154 samples including 11 Iranian sheep breeds (Kermani, Baluchi, Afshari, Karakul, Sanjabi, SiahKabud, LoriBakhtiari, Shal, Ghezel, Chios, GrayShiraz) and 54 Iceland sheep breeds, were used. The genomic data related to these genetic groups including Icelandic and Iranian sheep breeds were obtained from the iSheep database and the address https://disk.yandex.ru/d/3N2wEv0-9_NL0w, respectively. Quality control of genomic data and filtering was performed using the PLINK software. The individuals and SNP with more than 5% missing genotypes were excluded. Then, the data were screened based on Minor allele frequency (MAF) less than 1%, and Hardy-Weinberg equilibrium (HWE). From the total available data, including 552,847 SNP markers related to autosomal chromosomes with known locations from 204 animals, finally 527,292 SNP markers from 204 animals, with about 0.999376 genotyping rate, was remained for inter-population genetic differentiation and structure analyses. Then, data filtering was done for seprate genetic groups. In this edition of the data, 527,284 SNP markers related to 174 animals were endured to selection signature analysis. Genetic differentiation index analysis (FST) and principal component analysis (PCA) were performed to determine genetic groups using PLINK 1.9 software. Ubiased FST (θ) estimator statistic were used to explore the signs of selection. In order to better identify the selection signals, numerical values of SNP markers were averaged using the creeping window method up to a maximum length of 100 kb for adjacent SNPs. Only 0.1% of the genome regions with the highest values, were identified and determined as selection signatures. The genes related to selected genomic regions was extracted using the BIOMART online database corresponding areas in the sheep genome assembly (Oar 3.1). The detected genes, related to the selected regions, were surveyed by DAVID 2021 (The Database for Annotation, Visualization and Integrated Discovery) and STRING (Version 12) to identifying biological pathways and probably gene interaction networks. Results: According to the principal component analysis (PCA) results, the group with high reproductive performance (Icelandic sheep) is completely separate from the genetic group with low reproductive performance (Iranian sheep breeds). The complete differentiation between two groups with low and high reproductive performance can be related to the regional separation of these two groups. Kermani and Chios breeds (from the Chios breeding center in Kerman province of Iran) are seprated from other Iranian breeds with the second component. It was confirmed by further population structure analysis, with FST analysis and phylogeny tree extracted from the genetic distance determination information. The Icelandic breed had the greatest genetic distance with other studied breed. The GrayShiraz (SDK) and Kermani had the lowest and the highest genetically distance with Icelandic breed, respectively. In order to reduce the bias in selection signatures analysis, Kermani and Chios breeds was excluded from the further analysis. The results of principal component analysis (PCA), four distinc genetic groups was detected based on the information of the 1th component with one of the 2th to 6th components. According to the obtained results, the group with high reproductive performance or litter size (Icelandic) is completely separate from the genetic group with low reproductive performance (Iranian sheep breeds). The 2th to 6th components could demonstration differences among Iranian sheep breeds, and Kermani and Chios sheep breeds (raised in the Chios breeding center in Kerman province) were separated from the Iranian other sheep breeds. In the identified regions as selection signatures (N=55), including 0.1% of all studied markers, the number of 391 genes were identified. Of all detected genes, at least 13 genes including BMPR2, SLC26A4, WNT16, CREB3L4, PRLR, ACVR2B, PRKCSH, HOXA9, HOXA10, Dkks, KATNAL1, OSBP2 and W5PHY6_SHEEP genes were related to reproductive performance and probably litter size in sheep. Although no significant biological pathways (with high significance) related to litter size in sheep were identified, some of the identified genes have major effects on reproductive performance. Detected genes from this study and other complementary studies about involved genes on litter size in sheep, could be effective in designing breeding programs to improve reproductive performance. Conclusion: Survey on identified biological pathways related to genomic differences between Iranian native and Icelandic (which is considered one of the most significant multi-twin breeds in the world) sheep breeds, was shown that, the significant biological pathways involved in immunity, smell, and hemoglobin construction. Despite of the identification a large number of genes related to The reproductive performances, these genes did not involved in a significant biological pathways. However, although no significant biological pathways related to litter size in sheep were identified, some of the identified genes have major effects on reproductive performance. Detected genes from this study and other complementary studies about involved genes on litter size in sheep breeds could be effective in designing breeding programs to improve reproductive performance.

Objective Over the last decade, interest in detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. The aim of this study was to identify genomic region with a positive signature of selection related to wool quality in sheep breeds using selection signature FST and hapFLK methods. Materials and methods In this study, data from 146 Zandi and Merino animals genotyped using 50 K BeadChip were used to identify genomic regions under selection associated with wool quality traits. After quality control of the initial data using plink software, 46,793 SNP markers in 144 animals of sheep were finally entered for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under FST and hapFLK software packages, respectively. Candidate genes were identified by SNPs located at 0.1% upper range of FST and hapFLK values. GeneCards and UniProtKB databases were also used to interpret the function of the obtained genes. Results Using FST approach, we identified eight genomic regions on chromosomes 1 (two region), 1, 3 (two region), 10, 13, and 19 chromosomes, which were in the 99.9 percentile of all FST values. The identified candidate genes associated with wool trait in these genomic regions included POU1F1, FGF12, GNAS, LHX2, TMTC3, NBEA, MITF. Genes located in identified regions under selection were associated with the crimp of wool, growth and development of skin, fiber diameter, hair follicle development, growth and development of fiber, stimulation of collagen, regulation of epithelial cell and skin pigments, which can be directly and indirectly related to the trait of the wool quality. Also, the results of hapFLK statistics in this research led to the identification of four genomic regions on chromosomes 7, 10, 14, and 19. The identified candidate genes associated with the wool trait in these genomic regions included DUOX1, RHPN2 and LOC106991379. It was determined that they had different functions in collagen differentiation and gene expression of keratinocytes. Conclusions Various genes that were founded within these regions can be considered as candidates for selection based on function. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. The results of our research can be used to understand the genetic mechanism controlling wool traits and using these findings could potentially be useful for genetic selection in sheep for better clean fleece yield and mean fiber diameters.

Introduction: The principal aim of the dairy sheep industry worldwide is to produce high-quality milk. Mastitis is an inflammatory disease in dairy animals that occurs in response to infectious factors and has substantial impacts on animal health, and economic profitability. Somatic cell count has been used as an indirect method to control mastitis. Genetic resistance to mastitis involves linked biological mechanisms resulting from differences in mastitis response, which activate and regulate different levels of the immune response. Over the last decade, interest in identifying genes or genomic regions targeted by selection has grown. Identifying selection signatures can provide valuable insights into the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. This study aimed to identify effective genes and genomic regions under positive selection in two sheep breeds using selection signature methods. For this purpose, FST and hapFLK analyses were performed using the genome-wide single nucleotide polymorphisms (SNPs). Materials and methods: In this study, data from 585 sheep of different breeds were used to identify genomic regions under selection related to the somatic cell counts in milk. Illumina ovine Bead Chip 50K was used to determine the genotype of the samples. The genomic information of sheep breeds was extracted from the Zenodo database. Quality control on genotyped samples was performed using PLINK v1.9. SNPs with a minor allele frequency (MAF) of less than 0.02 and those with a call rate of less than 0.97 were excluded. In addition, individuals with more than 10% missing genotype data were removed. SNPs that did not conform to Hardy–Weinberg equilibrium (P<10-6) were also eliminated. Following these quality control measures, 41,673 SNPs from 585 sheep were retained for further analysis. To identify the signatures of selection, two statistical methods of FST and hapFLK were used under the software packages FST and hapFLK, respectively. The candidate genes were identified using PLINK v1.9 software and the Illumina gene list in R by SNPs located in the 0.01 percentile of FST and hapFLK values. In addition, the latest published version of the animal genome database was used to define QTLs associated with economically important traits at identified loci. The GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes. Results and discussion: Using the FST approach, nine genomic regions on chromosomes 2, 5, 7, 10, 11, 13 (two regions), 17, and 22 were identified. The identified candidate genes associated with somatic cell count in these genomic regions included IL11RA, CDC16, CARD14, BTRC, OTUD4, COL23A1, LACTB, and PRELID3B. Some of the genes located in the identified selection regions were associated with the immune system, innate immune response, inflammation response, cancer disease, and milk production. Some of these genes in the selected regions were consistent with previous studies. The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including milk somatic cell count, udder height and depth, clinical mastitis, bovine tuberculosis susceptibility, and heat tolerance. Moreover, the results of hapFLK statistics in this research led to the identification of six genomic regions on chromosomes 3, 4, 5, 7, 10, and 13. The identified candidate genes associated with the somatic cell count in these genomic regions included FAM49A, CDK6, and DLGAP5. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to innate immune and various cancers. Conclusions: Different genes that emerged in the aforementioned regions can be considered candidates for selection based on their function. It was found that most of the selected genes were consistent with some previous studies and were involved in production traits. However, further investigation is recommended to determine the exact function of the identified genes and QTLs. These areas should also be confirmed by other independent studies using larger samples. In general, the data from this study may be used in research on genomic selection and genomic regions associated with mastitis in dairy sheep and in further reviews and evaluations for the improvement of dairy sheep production.

ObjectiveLactation persistence and milk production are among the most economically important traits in the dairy sheep industry. Lactation persistency is defined as the ability of a sheep to maintain milk production at a high level after reaching peak production. Identifying selection signatures can provide valuable insights about the genes or genomic regions that have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. This study aimed to identify genomic regions with positive selection signatures related to lactation persistency and milk production in sheep breeds using the FST method.   Materials and Methods In this study, data from Zandi (96 samples), Chios (317 samples), and Valle del Belìce (481 samples) sheep, genotyped using a 50K BeadChip, were used to identify genomic regions under selection associated with milk traits. After quality control of the initial data using PLINK software, 36,605 SNP markers in 880 sheep were retained for further analysis. To identify the selection signatures, the statistical method of FST was employed using the FST software package. Candidate genes were identified by SNPs located in the top 0.1% of FST values. The GeneCards and UniProtKB databases were also used to interpret the functions of the identified genes.     ResultsUsing the FST approach, we identified ten genomic regions on chromosomes 1, 2, 4, 6, 8, 9, 11, 13, 17, and 20 that were in the 99.9th percentile of all FST values. The identified candidate genes associated with milk and lactation persistency traits in these genomic regions included FAIM, CEP70, TLR4, SLC37A3, KDM7A, HERC6, NT5DC1, SPIDR, SMOX, and RNF144B. Genes located in these identified regions under selection were associated with milk production, milk fat synthesis, lactation persistency, inflammatory response, and immune system functions, which can be directly and indirectly related to milk production traits. Additionally, a review of the extracted QTLs showed that these QTLs are involved in economically important traits in sheep, such as milk yield, milk fat composition, milk fat percentage, and milk yield persistence.   ConclusionsThe genes identified within these regions can be considered candidates under selection based on their functions. However, further association and functional studies are necessary to confirm the implications of the genes obtained from this analysis. The results of this research can be used to understand the genetic mechanisms controlling milk production and persistency traits. These findings could potentially support genetic selection in sheep for improved milk yield and lactation persistence following peak lactation.

Introduction Understanding the genomic features of livestock is essential for successful breeding programs and conservation. Artificial selection is one of the major forces modifying the genetic composition of livestock populations. The identification of selection targeted genomic regions is one of the main aims of biological research. Domestication and selection have significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans left detectable signatures on the genome of modern goat. Understanding the genomic features of livestock is essential for successful breeding programs and conservation. Artificial selection is one of the major forces modifying the genetic composition of livestock populations. Identifying genes under selection could be useful to elucidate their impact on phenotypic variation. Over the last decade, interest in the detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. This study aimed to identify effective genes and genomic regions on positive signature of selection in Murciano-Granadina and Barki goats using selection signature method.Material and Methods: In this study, data from 923 Murciano-Granadina and 68 Barki goats genotyped using Caprine 50 K Bead Chip were used to identify genomic regions under selection associated with important traits. Quality control measures were performed in Plink by setting an animal call rate of 0.90, SNP call rate of 0.95, and SNPs with minor allele frequencies (MAF) lower than 0.05 or that do not conform to the Hardy–Weinberg expectation (P value ≤ 0.000001) and unknown position. After quality control of the initial data using Plink software (v1.90; http://pngu.mgh.harvard.edu/purcell/plink), 43,170 SNP markers were finally entered for further analysis. To identify the signatures of selection, the statistical method FST was used under R software packages. Candidate genes were identified by SNPs located at 1% upper range of FST using Plink v1.9 software and the gene list of Illumina in R. Additionally, the latest published version of Animal genome database was used for defining QTLs associated with economically important traits in identified locations. GeneCards (http://www.genecards.org) and UniProtKB (http://www.uniprot.org) databases were also used to interpret the function of the obtained genes.Results and Discussion Using FST approach, we identified seven genomic regions on chromosomes 4, 6, 14, 15 (two regions per chromosome), 17, and 23 chromosomes. Candidate genes were detected. Some of the genes located in identified regions under selection were associated with milk yield, fat yield and fat metabolism, milk protein percentage and cholesterol milk content. Some of the genes under selection were found to be consistent with some previous studies|. Results of gene ontology analysis identified two biological pathways namely skeletal system development and calcium channel complex with two important KEGG pathways including glucagon signaling pathway and AMPK signaling pathway which play an important role in glucose metabolism and homeostasis and skeletal system development. Conclusion: By the way, various genes that were found within these regions can be considered as candidates under selection based on function. Most of the genes under selection were found to be consistent with some previous studies and to be involved in several processes such as milk yield and metabolic pathways. Also, a survey on extracted QTLs showed that these QTLs involved in some economical important traits in goat such as milk yield and milk composition traits. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes and survey on QTLs related to selected regions. However, will be necessary to carry out more association and functional studies to demonstrate the implication of genes obtained from association analyses. Using these findings can accelerate the genetic progress in the breeding programs and can be used to understand the genetic mechanism controlling this trait. The results of our research can be used to understand the genetic mechanism controlling milk and composition traits and considering, this study supported previous results from genome scan of production traits, also revealed additional regions, using these findings could potentially be useful for genetic selection in goat for better milk yield.

Descriptive statistics is the process of summarizing gathered raw data from a research and creating useful statistics, which help the better understanding of data. According to the types of variables, which consist of qualitative and quantitative variables, some descriptive statistics have been introduced. Frequency percentage is used in qualitative data, and mean, median, mode, standard deviation; standard error, variance, and range are some of the statistics which are used in quantitative data. In health sciences, the majority of continuous variables follow a normal distribution. skewness and kurtosis are two statistics which help to compare a given distribution with the normal distribution.

Introduction: The model for screening antidepressant-like activity in pre-clinical drug studies include, rat forced swimming test (FST). The reports on N-acetylcysteine (NAC) as an antioxidant supplement in stress related disorder is well documented. This study was aimed at potential antidepressant mechanism of N-Acetyl Cysteine (NAC), a glutamate precursor on FST animal model for screening antidepressant drugs using fluoxetine, a selective serotonin reuptake inhibitors (SSRIs) as standard antidepressant drug. Methods: Thirty adult male Wistar rats used for this study were randomly divided into six groups each with five (n=5) rats. The control group (A) received 1 ml of normal saline daily, group B served as the FST model, group C received 200mg/kg/day of NAC, group D received 20mg/kg/day of fluoxetine, group E the FST model treated with 200mg/kg/day of NAC, and F is the FST model treated with 20mg/kg/day of fluoxetine. Drugs were given orally. The effects of NAC on brain weights, the FST paradigms, sucrose preference test (SPT) for anhedonia were assessed and data analyzed using ANOVA where Tukey post-hoc test for statistical significance was set at (p < 0. 05). The brains fixed in 4% paraformaldehyde, were processed and the paraffin embedded tissue were serially sectioned at 5 μ, m thick to be stained using Haematoxylin and Eosin (H and E) stain, immuno-histochemistry for synaptophysin (p38) and astrocytes (GFAP) activities in the prefrontal cortex (PFC). Results: Findings showed that NAC prevented FST-induced anxiety-like behaviors demonstrated by an increased SPT (that alleviates anhedonia), mobility time, and reduced immobility time. NAC caused an increase in brain weights and prevented FST-induced neurodegeneration, the proliferation of reactive astrocytes, and diminished synaptophysin immunoreactivity in the PFC similar to that seen in fluoxetine a standard anti-depressant drug. Conclusion: NAC treatment significantly exhibits its neuroprotective mechanism via inhibiting the proliferation of reactive astrocytes, which protects neurons and synapses from oxidative tissue damage induced by FST, hence an increase in synaptophysin activity that culminates in increased neural activity, increased SPT, and reduced immobility time.